PROJECT SUMMARY Most bacteria are found in complex microbial communities, where social interactions between community members shape community function and behavior. One of the mechanisms by which bacteria regulate these interactions is through quorum sensing, a type of cell-cell signaling that regulates behaviors in a population density-dependent manner. Quorum sensing contributes to pathogenesis of many plant and animal pathogens and for this reason has been the target of efforts to develop novel antivirulence therapeutics. Previously, studies of quorum sensing, including infection models, have been primarily carried out using clonal populations. Results of those studies have been useful to elucidate the molecular biology of quorum sensing but provide a limited understanding the role of quorum sensing in more complex communities such as those from the environment and infections. Direct studies of naturally occurring mixed microbial communities present many challenges. A combination of new technologies in genomics and genetics and the development of laboratory models or `synthetic ecology' approaches have advanced progress on studies of non-clonal bacterial populations. The PI has developed laboratory models to study quorum sensing in mixed-strain and mixed-species populations to define how quorum sensing promotes survival in these communities. A major result of these studies is that quorum sensing increases resistance to antibiotics and that this regulation alters the dynamics of competition among and between strains of soil bacteria. Quorum sensing also regulates antibiotic resistance in the opportunistic pathogen P. aeruginosa, and this regulation could be important in the face of antibiotic therapy during infections. The studies in this proposal will build on these preliminary results and use laboratory models and clinical infection isolates to study quorum-sensing control of antibiotic resistance. The studies will provide a mechanistic understanding of quorum-regulated antibiotic resistance, define how quorum-sensing systems adapt under selection by antibiotics, and determine how quorum sensing alters resistance phenotypes among individual members of a population. The results will provide a more complete picture of how quorum sensing alters population dynamics in complex communities and in patient infections. This information is needed to develop novel disease treatment strategies that rely on manipulating the quorum-sensing systems of pathogens. The results will have implications in the field of quorum sensing, for understanding social behavior in a broader sense, and finally, for understanding how social behaviors can be targeted for effective treatment of polymicrobial infections.